code: def evaluate(
      *,
      rng: jnp.ndarray,
      config: ml_collections.ConfigDict,
      model_cls: Type[base_model.BaseModel],
      dataset: dataset_utils.Dataset,
      workdir: str,
      writer: metric_writers.MetricWriter,
  ) -> dict[str, Any]:
      """Evaluates the model.
  
      This function loads a pretrained model, optionally overrides some arguments
      related to evaluation in its original config, and then evaluates the model
      on the specified dataset.
  
      Args:
        rng: Jax rng key.
        config: Configurations for evaluation. Can be reused to override some
          settings from the training config.
        model_cls: Model class; A model has a flax_module, a loss_fn, and a
          metrics_fn associated with it.
        dataset: The dataset that has train_iter, eval_iter, meta_data, and
          optionally, test_iter.
        workdir: Directory for checkpointing.
        writer: CLU metrics writer instance.
  
      Returns:
         eval_summary: Dictionary with the evaluation summary
      """
      lead_host = jax.process_index() == 0
      # Build the loss_fn, metrics, and flax_model.
      model = model_cls(config, dataset.meta_data)
  
      # Initialize model.
      train_state, current_step = restore_train_state(
          rng, config, model, dataset
      )
      # Replicate the optimzier, state, and rng.
      train_state = jax_utils.replicate(train_state)
      eval_step_pmapped = jax.pmap(
          functools.partial(
              trainer.eval_step,
              flax_model=model.flax_model,
              metrics_fn=model.get_metrics_fn("validation"),
              all_gather=config.get("global_metrics", False),
              debug=config.debug_eval,
          ),
          axis_name="batch",
          # We can donate the eval_batch's buffer.
          donate_argnums=(1,),
      )
      inference_step_pmapped = jax.pmap(
          functools.partial(
              inference_step,
              flax_model=model.flax_model,
              debug=config.debug_eval,
          ),
          axis_name="batch",
          donate_argnums=(1,),
      )
      # If `global_metrics` are set in the config and we are the lead host
      compute_global_metrics = False
      if config.get("global_metrics", False) and lead_host:
          compute_global_metrics = True
      if compute_global_metrics:
          global_metrics_evaluator = (
              tasseo_train_utils.TasseoGlobalEvaluator(
                  config.global_metrics
              )
          )
      # Sync model state across replicas.
      train_state = train_utils.sync_model_state_across_replicas(
          train_state
      )
      eval_summary = {}
      # Ceil rounding such that we include the last incomplete batch.
      total_eval_steps = int(
          np.ceil(
              dataset.meta_data["num_eval_examples"] / config.batch_size
          )
      )
      eval_metrics = []
      if not config.save_predictions:
          for s in range(total_eval_steps):
              eval_batch = next(dataset.valid_iter)
              e_metrics, e_output, e_batch = eval_step_pmapped(
                  train_state, eval_batch
              )
              eval_metrics.append(
                  train_utils.unreplicate_and_get(e_metrics)
              )
              logging.info("eval metircs at step %d", s)
              if compute_global_metrics:
                  # Unreplicate outputs of eval_step_pmapped that are coming from
                  # `lax.all_gather`, fetch to the host and add to the Evaluator:
                  e_batch_mask = train_utils.unreplicate_and_get(
                      e_batch["batch_mask"]
                  ).astype(bool)
                  global_metrics_evaluator.add_batch_of_examples(
                      target=train_utils.unreplicate_and_get(
                          e_batch["label"]
                      )[e_batch_mask],
                      output=train_utils.unreplicate_and_get(e_output)[
                          e_batch_mask
                      ],
                  )
                  del e_batch, e_output, e_batch_mask
          eval_global_metrics_summary = None
          if compute_global_metrics:
              if dataset.meta_data["num_eval_examples"] != len(
                  global_metrics_evaluator
              ):
                  logging.warning(
                      "Number of eval (valid/test) examples in the dataset metadata is "
                      "%d, however the global evaluator captured only %d of them",
                      dataset.meta_data["num_eval_examples"],
                      len(global_metrics_evaluator),
                  )
              eval_global_metrics_summary = (
                  global_metrics_evaluator.compute_metrics(
                      clear_annotations=True
                  )
              )
  
          eval_summary.update(
              train_utils.log_eval_summary(
                  step=current_step,
                  eval_metrics=eval_metrics,
                  extra_eval_summary=eval_global_metrics_summary,
                  writer=writer,
              )
          )
          del eval_metrics, eval_global_metrics_summary
      else:
          compute_similarity_scores(
              train_state=train_state,
              iterator=dataset.valid_iter,
              eval_step_fn=inference_step_pmapped,
              eval_steps=total_eval_steps,
              workdir=workdir,
              lead_host=lead_host,
          )
      writer.flush()
      # Wait until computations are done before exiting.
      train_utils.barrier_across_hosts()
      # Return the train and eval summary after last step for regression testing.
      return eval_summary
  
imports: import datetime
  import functools
  import os
  import pickle
  from typing import Any, Callable, Optional, Type
  from absl import logging
  from clu import metric_writers
  from flax import jax_utils
  import flax.linen
  import jax
  import jax.numpy
  import jax.profiler
  import ml_collections
  import numpy
  from scenic.common_lib import debug_utils
  from scenic.dataset_lib import dataset_utils
  from scenic.model_lib.base_models import base_model
  from scenic.projects.tasseo import classification_trainer
  from scenic.projects.tasseo import train_utils
  from scenic.train_lib import optimizers
  from scenic.train_lib import pretrain_utils
  from scenic.train_lib import train_utils
  import tensorflow
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate() and returns: eval_summary